At present, chat generative pre-trained transformer (ChatGPT) as a representative of the rapid development of large language models, is widely used in stock market investment, algorithmic trading, risk management and other fields. This provides financial investors with new decision-making tools and investment paths. In this paper, we construct an investment trading model based on the bidirectional encoder representation from transformers (BERT) model and chat generative pre-trained transformer (ChatGPT) for the Chinese stock market, and realize the trading signals from financial news text data and traditional financial data. For the text data, the daily financial news is captured and matched with the corresponding stock codes. Secondly, we input the news text data into the trained fine-tuning BERT (FTBERT) model to get the sentiment tendency of each news item, and select the positive financial news as the positive investment trading signals. For the traditional financial data, we use the advanced parsing capability of chat generative pre-trained transformer (ChatGPT) to analyze the historical data of Chinese stock market. By adjusting the prompt to read the data, the key factors for stock investment are constructed, and the daily scores of each stock are output. Finally, the daily investment signals of each stock are obtained based on different data types, which are used as the basis for constructing investment portfolios and building effective investment strategies. The empirical results show that chat generative pre-trained transformer (ChatGPT) effectively determine the sentiment tendency of text. The fine-tuned model can effectively assist quantitative investment and bring investors excessive returns. This study attempts to apply big language modeling to financial investment and shows its potential value in generating stock investment signals. With the continuous development of technology and changes in the market environment, this artificial intelligence-based investment strategy will continue to evolve and create more value for investors.

Large models, exemplified by ChatGPT, represent a significant breakthrough in general generative artificial intelligence technology. Their far-reaching implications extend into diverse facets of human production, lifestyle, and cognitive processes, prompting a transformative paradigm shift in the realm of economic research. Originating from the convergence of big data and artificial intelligence, these large models introduce a novel approach to systemic analysis, particularly adept at scrutinizing intricate human economic and social systems. We first discuss the fundamental characteristics and development paradigms of ChatGPT and large models, focusing on how these models effectively tackle the methodological challenges posed by the "curse of dimensionality". We then delve into how ChatGPT and large models will influence the paradigm of economic research. This includes a shift from the assumption of the rational economic man to an AI-driven "human-machine hybrid" economic agent, from the isolated economic individual to the socio-economic individual whose behaviors are measurable, from the separation of macroeconomics and microeconomics to their integration, from the separation of qualitative and quantitative analysis to their unification, and from the long-dominant "small-model" paradigm to a "large-model" paradigm in economic research. We also cover the increasing significance of computer algorithms as a prominent research paradigm and method in economics. Finally, we point out the limitations inherent in artificial intelligence technologies, including large models, when employed as a research method in economics and the broader social sciences.

Bilevel Optimization recently became a very active research area. This is mainly due to its important applications from machine learning. In this paper, we give a gentle introduction to algorithms, theory, and applications of bilevel optimization. In particular, we will discuss the history of bilevel optimization, its applications in power grid, hyper-parameter optimization, meta learning, as well as algorithms for solving bilevel optimization and their convergence properties. We will mainly discuss algorithms for solving two types of bilevel optimization problems: lower-level problem is strongly convex and lower-level problem is convex. We will discuss gradient methods and value-function-based methods. Decentralized and federated bilevel optimization will also be discussed.

Commodity is an important part of industrial production and financial investment, and accurate commodity price forecasting is of great significance to safeguard industrial production and help investors avoid risks. However, most of the existing commodity price forecasting models are point-value models based on closing prices, which ignores the volatility information. Therefore we propose a heteroskedasticity threshold autoregressive interval model with exogenous variables (HTARIX) and apply it to the commodity markets. We also construct a test statistic based on interval-valued data to test whether there is conditional heteroskedasticity in the model, and propose a generalized minimum $D_K$ distance estimation. The advantage of our model is that it can capture the conditional heteroskedasticity and nonlinear features of interval-valued time series models. Compared with the point-valued models, our method contains more information of the data. The empirical results imply that HTARIX model performs better than other comparative models in interval-valued commodity price forecasting.

The main purpose of this paper is using the elementary methods, the number of the solutions of some congruence equations and the properties of the classical Gauss sums to study the calculating problem of the fifth power mean of one kind two-term exponential sums, and give the exact calculating formula for it.

Existing research has shown that political crisis events can directly impact the tourism industry. However, the current methods suffer from potential changes of unobserved variables, which poses challenges for a reliable evaluation of the political crisis impacts. This paper proposes a panel counterfactual approach with Internet search index, which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest. It also provides a tool to examine potential channels through which the crisis may affect tourist outflows. This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea. Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place. This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.

This paper compares and analyzes the differences in stock pricing between news sentiment and social media sentiment in two different time dimensions, daily and monthly, using individual sentiment data from the Thomson Reuters MarketPsych Indices and trading data from the US stock market from 2010 to 2019. The empirical results indicate that social media sentiment performs better at the daily level than news sentiment, and news sentiment has a stronger explanatory power on stock returns at the monthly level than social media sentiment. Specifically, at the daily level, this paper constructs news sentiment factor and social media sentiment factor, and finds that social media sentiment factor still exhibits significant excess returns under the Fama-French five-factor model, while news sentiment factor no longer exhibits excess returns. In addition, social media sentiment factor can explain most market anomalies at the daily level, while news sentiment factor cannot. In order to investigate the reasons, this paper conducts a Granger causality test, indicating that the response speed of social media sentiment factor is 3 to 4 trading days faster than that of news sentiment factor. At the monthly level, this paper finds that news sentiment improves its ability to explain anomalies, while the explanatory power of social media decreases significantly. In addition, for volatility anomalies and idiosyncratic volatility anomalies, the monthly news sentiment factor has a significant explanatory power, while the explanatory power of the monthly social media sentiment factor is not significant.

It is with great pleasure and admiration that we celebrate the 60th birthday of Professor Lihua Xie, a distinguished researcher and visionary leader in the field of robust control and estimation. Prof. Xie’s remarkable journey, marked by outstanding achievements and groundbreaking contributions, has left an indelible mark on the world of engineering and academia.
Prof. Xie’s academic odyssey began at Nanjing University of Science and Technology, where he earned his bachelor’s and master’s degrees in 1983 and 1986, respectively. His pursuit of knowledge led him to the University of Newcastle, Australia, where he obtained his PhD in 1992. Since 1992, he has been a cornerstone of Nanyang Technological University (NTU), Singapore, currently serving as a distinguished professor in the School of Electrical and Electronic Engineering and as the Director of the Centre for Advanced Robotics Technology Innovation (CARTIN), NTU.
One of Prof. Xie’s pivotal contributions lies in the realm of robust control and estimation. His early work in the early 1990s addressed robust solutions for systems with parametric uncertainties, providing a profound understanding of how uncertainty influences control system performance. His pioneering research not only illuminated the impact of uncertainty but also offered effective strategies, particularly for parametric uncertainty, ensuring the robustness of control systems. Prof. Xie was among the first to develop robust estimation techniques for systems grappling with parametric uncertainties, influencing researchers globally since the 1990s.
In the past two decades, Prof. Xie, alongside his co-author, established a groundbreaking equivalence between quantized feedback and robust control. This breakthrough extended the applicability of existing robust control theory to the analysis and design of control systems operating under quantized feedback. His work also unraveled the intricate interplay among data rate, network topology, and agent dynamics in multi-agent consensus - a fundamental challenge in cooperative control. Prof. Xie’s research provided answers to crucial questions, such as determining the minimal data rate and network topology for multi-agent consensus, along with corresponding coding and decoding schemes.
The spectrum of Prof. Xie’s impact extends to compressive sensing, where he and his student established a phase transition relationship between sparsity and recoverability for complex signals. Their continuous compressive sensing algorithms and Vandermonde decomposition theory for multi-level Toeplitz matrices have found applications in array signal processing, marking another significant milestone in his illustrious career.
Beyond theoretical endeavors, Prof. Xie’s practical innovations have revolutionized localization and unmanned systems. His research group’s developments include a WiFi-based indoor positioning system, multi-modality sensor fusion technology, and a fully integrated navigation solution for UAVs. These innovations have found applications in diverse fields, from structure inspection and delivery using UAVs to a low-cost universal navigation system for AGVs in logistics and manufacturing.
In the realm of research and development leadership, Prof. Xie’s impact is equally profound. He is the founding Director of the Delta-NTU Corporate Laboratory for Cyber-physical Systems, which focuses on the development of smart manufacturing and smart learning technologies for industry. Additionally, Prof. Xie established the Centre for Advanced Robotics Technology Innovation, where he currently serves as the Director. The center’s mission is to pioneer advanced sensing and perception technologies, as well as collaborative robotics technologies, with applications in logistics, manufacturing, and elderly care.
As an accomplished researcher, Prof. Xie has demonstrated unparalleled dedication to serving the research community. His extensive editorial roles, including a founding Editor-inChief for Unmanned Systems and Associate Editor for Sciences China - Information Science, showcase his commitment to advancing scientific knowledge. He has played pivotal roles in various editorial boards, such as IET Book Series in Control and esteemed journals like IEEE Transactions on Automatic Control and Automatica.
Prof. Xie’s impact extends beyond editorial responsibilities; he has been a distinguished IEEE Distinguished Lecturer, a Board of Governors member for the IEEE Control System Society, and Vice President since January 2024. His leadership roles also include serving as General Chair of significant conferences, including the 62nd IEEE Conference on Decision and Control in December 2023.
His professional achievements, recognized by peers worldwide, include fellowships in the Academy of Engineering Singapore, the Institute of Electrical and Electronics Engineers (IEEE), International Federation of Automatic Control (IFAC), and the Chinese Automation Association (CAA).
In celebration of Prof. Xie’s 60th birthday, we invited 17 papers from friends and colleagues for this special issue. As editors, we extend our deepest gratitude to all the authors for their invaluable contributions. Special thanks to the Journal of Systems Science & Complexity editorial office, including Prof. Xiao-Shan Gao (Editor-in-Chief), Prof. Yanlong Zhao (Managing Editor), and Ms. Guoyun Wu (Editorial Director), for their steadfast support from the conception to the publication of this special issue.
On this momentous occasion, we express our profound appreciation for Prof. Lihua Xie for his unwavering commitment to advancing knowledge and look forward to the continued brilliance and innovation in the next chapters of his illustrious career.
Happy Birthday, Prof. Lihua Xie!

In the background of the rise of the anti-globalization trend in the postepidemic era, China's economic development is expected to rely more on the increase in the degree of dependence on the pull of domestic demand, so the study on how to promote the level of consumption of the residents and the expansion of domestic demand is also becoming more and more critical. The deep integration of digital economic development and traditional industries can release a sizable "digital dividend", colossal energy, is the expansion of domestic demand, the realization of the "domestic cycle" of the critical potential driving force. Given this, based on the China Family Panel Studies (CFPS) database, this paper constructs the digital economy development level index at the provincial level in China. It empirically examines the theoretical analysis and empirical test on the effect of digital economy development on residents' household consumption. The study finds that: (ⅰ) Digital economy development can promote residents' household and per capita consumption. This conclusion still holds after a series of robustness tests. (ⅱ) The development of the digital economy can positively impact residents' consumption level, mainly through improving the quality of residents' income. However, optimizing the consumption environment is not the main reason for the digital economy to promote consumption.(ⅲ) From the heterogeneity analysis, digital economy development has a more noticeable effect on the consumption enhancement of regions with low unemployment rates and households with low labor costs. (ⅳ) This paper further discusses the impact of digital economic development on the consumption structure and finds that digital economic development makes it diffcult to enhance residents' subsistence consumption but significantly increases the developmental consumption expenditures of income groups at all levels and also substantially promotes the enjoyment expenditures of high-income households. The findings of this paper provide theoretical support and a decision-making basis for further utilizing digital economic development to release consumption potential.

With the explosive growth of investable assets and asset information, portfolio selection faces the dual challenges of high dimensionality in both assets and characteristics. This paper proposes a portfolio selection framework based on machine learning and asset characteristics. Leveraging the inherent advantages of machine learning, the framework utilizes asset characteristics to directly predict portfolio weights, bypassing return distribution prediction in the conventional two-step portfolio management paradigm. The framework is applied to asset allocation research in the Chinese stock market. The research results show that: 1) The proposed investment strategies capture incremental information within high-dimensional characteristics and uncover both linear and non-linear relationships between asset characteristics and portfolio weights, resulting in a significant enhancement of investment performance. 2) Trading friction-related characteristics are the most important indicators for predicting portfolio weights. 3) These strategies yield higher returns on stocks with stricter arbitrage restrictions while exhibiting lower sensitivity to changes in macroeconomic conditions. Under other economic constraints, these strategies remain robust. This paper expands the research framework of modern portfolio theory, contributing to the development of artificial intelligence and quantitative investment.

This study investigates the relationship between climate change and systemic risk in China's financial system. First, it examines the responsiveness of systemic risk in the banking, securities, and insurance sectors to extreme climate events, assessing how different financial industries withstand such disasters. The findings confirm that certain extreme climate events can exacerbate systemic financial risk. Second, by constructing a nonlinear autoregressive distributed lag (NARDL) model, this study analyzes the impact of the performance of green and brown market stock indices on the systemic risk of financial sub-sectors. The results indicate that in the short term, an increase in the risk of brown assets and a decrease in their indices significantly amplify systemic risk in the financial industry. However, in the long term, an increase in the brown asset index raises systemic risk in the banking sector, while an increase in the green asset index reduces systemic risk in the securities sector. Furthermore, a reduction in green asset risk significantly lowers systemic risk in the banking sector. In addition, this study underscores the importance of policies addressing the increasing frequency and severity of climate-related disasters. It recommends differentiated financial prudential regulations for green and brown sectors to minimize transition risks associated with climate policy implementation while mitigating physical risks. This approach is crucial to improve risk management frameworks in the financial industry, thereby reducing the impact of both physical and transition risks on systemic risk.

In the digital economy, the emergence of digital currencies has attracted considerable attention from both investors and researchers. However, their high volatility characteristics present new challenges in investment decision-making and risk assessment. To capture the characteristics comprehensively, this paper proposes a novel approach for constructing confidence regions for interval-valued variables based on the exponentially decay weighted bootstrap. The coverage area of the confidence regions and tail quantiles provide new indicators for assessing the volatility and tail risks in the market. Empirical results using Bitcoin as a case study demonstrate the proposed approach outperforms other traditional point-based methods such as exponential weighted moving average in measuring the uncertainty and intraday price volatility. Furthermore, the derived tail quantiles exhibit superior predictive performance for tail risk compared to Value-at-risk methods and the exponential weighted moving average, as evidenced by various tests. The proposed methodology not only contributes a new statistical tool for analyzing digital currency volatility but also provides novel perspectives for extreme risk management in financial markets.

It is conjectured by Professor Zhi-Wei Sun that for each given odd prime $p>100, $ there always exists an solution $(x,y,z)\in[1,p]^3$ to the Pythagoras equation $x^2+y^2=z^2$ such that $x,y,z$ are quadratic residues or non-residues modulo $p$ respectively (eight cases in total). In this paper, we are able to prove the above assertion for all sufficiently large primes $p$, and the method is based on the recent Burgess bound for character sums of forms in many variables due to Lillian B. Pierce and Junyan Xu.

This paper proposes the real-time Realized EGARCH-MIDAS (RT-REGARCH-MIDAS) model which adequately captures the information content of high-frequency data, the current return information and the long memory of volatility to model and forecast Chinese stock market volatility. An empirical analysis based on the 5-minute high-frequency data of the Shanghai Stock Exchange Composite Index (SSEC) and the Shenzhen Stock Exchange Component Index (SZSEC) shows that the RT-REGARCH-MIDAS model outperforms a variety of competitor models in fitting the return data and can describe the stock market volatility better. Using robust loss functions and the model confidence set (MCS) test, the paper compares the out-of-sample forecasting ability of the model and other competitor models for Chinese stock market volatility. Our empirical results show that accounting for the information content of high-frequency data, the current return information and the long memory of volatility plays an important role in forecasting stock market volatility. As a consequence, the proposed RT-REGARCH-MIDAS model performs the best in forecasting Chinese stock market volatility. Further, according to the robustness checks, the superior volatility forecasting ability of the model is robust to alternative realized measure, alternative forecast windows, alternative MIDAS lags, alternative forecasting horizons and out-of-sample R² test. Finally, a volatility timing strategy shows that the proposed model yields more significant economic value of portfolio compared to the other models.

This article aims to explore the impact of the internet on income inequality among rural households. Through the analysis of data from China Family Panel Studies, it is found that although the internet can significantly alleviate the inequality in total income and wage income among rural households, its effect on alleviating inequality in entrepreneurial income is limited, and it may exacerbate inequality in household property income. Based on this finding, this article analyzes the mechanism of its impact from the perspective of household income sources, revealing that the internet mainly reduces the wage income gap by pulling rural labor force into the non-agricultural sector, thereby alleviating household income inequality. Meanwhile, households with original capital accumulation are more likely to benefit from the internet, which exacerbates property income inequality. In addition, this article introduces the causal forest algorithm and, from the perspective of human capital, analyzes the heterogeneous effects of the internet on individual-level inequality in wage income and property income among rural households. The results show that the alleviation of wage income inequality is mainly manifested in households with low human capital, while the exacerbation of property income inequality is mainly manifested in households with high human capital.

This article is based on the 2017 and 2019 China Household Finance Survey (CHFS) data. It conducts a systematic study on the impact and mechanism of commuting on residents' subjective well-being in China from urban-rural heterogeneity and demographic heterogeneity perspectives. Research has found that, firstly, the three aspects of commuting: i.e., commuting time, commuting distance, and commuting method have a significant impact on residents' subjective well-being. Commuting time has a significant negative effect on residents' subjective well-being. In contrast, longer commuting distance compensates for the negative impact of long-distance commuting on residents' subjective well-being by enhancing the utility of other aspects. Among commuting methods, at present, public transportation has the most significant inhibitory effect on residents' subjective well-being. Secondly, the analysis of the mechanism of action shows that the impact of commuting time, commuting distance, and commuting method on residents' subjective well-being shows substantial heterogeneity due to differences in regional location and individual characteristics. The impact is more significant in urban areas, eastern regions, high-priced housing areas, male residents, married residents, and residents with children. Thirdly, further exploring the external conditions that enhance the subjective well-being of residents through commuting, excessive construction of bridges, overpasses, etc., is not conducive to improving the quality of commuting but may damage the subjective well-being of residents.

Financial Technology (FinTech) combines financial, inclusive and technological aspects. Under the new development pattern, promoting China's common prosperity cannot be separated from the support of FinTech. Based on the provincial panel data of China from 2011 to 2020, this paper first constructs the common prosperity index from the three dimensions of development, sharing and sustainability, and then examines the impact and function mechanisms of FinTech development on China's common prosperity. The results show that FinTech development can significantly promote China's common prosperity. Further analysis reveals that the coverage of FinTech has a more significant promoting effect on China's common prosperity, and the promotional effect of FinTech development is more obvious on the sustainability of common prosperity, followed by development and the weakest sharing. The results of mechanism analysis show that FinTech development can promote human capital accumulation, enhance marketization, promote the development of the circulation industry, boost residents' consumption, and thus contribute to China's common prosperity by smoothing the domestic circulation. Heterogeneity testing indicates that there exists a regional Matthew effect in FinTech development, but this effect can be mitigated by increasing innovation activities. Therefore, this paper proposes to continuously improve the quality and efficiency of FinTech development, smooth the domestic circulation, strengthen the tilt of digital basic resources, and enhance regional innovation vitality, so as to make FinTech more effective in adding impetus to the realization of China's common prosperity.

With the rising labor costs and increasing resource and environmental constraints in China, coupled with geopolitical conflicts, related industries or production processes are shifting to emerging economies such as Southeast Asia, South Asia, and Mexico. Among these, India's development potential has garnered significant attention, and the "China-to-India industrial relocation model" in the global industrial chain poses a greater impact and threat to China. This paper constructs a pre-quantitative model to measure the impact of industrial relocation on the home country. It designs three scenarios—Ultra-long-term, medium-to-long-term, and short-to-medium-term—And uses counterfactual analysis to assess the impact of India's absorption of China's industrial relocation on China's GDP and employment under different scenarios. The research results indicate that the relocation of industries from China to India will generate significant socio-economic shocks. In the ultra-long-term, this industrial transfer could lead to a 15.6% reduction in China's GDP, a 16.8% decrease in the overall income of the workforce, and a reduction in the number of employed people by 110 million. The impacts are also substantial in the medium-to-long-term and short-to-medium-term scenarios. By sectors, the relocation of low and medium-low R&D intensity manufacturing sectors has a significant impact on the Chinese economy in both the short-to-medium and medium-to-long term perspectives. The relocation of high R&D intensity manufacturing sectors, represented by the computer industry, also causes considerable negative effects on the Chinese economy in the ultra-long-term perspective. This quantitative analysis helps anticipate the economic impact of future changes in industrial layout on China's economy and facilitates the development of preemptive strategies. Based on the medium-to-long-term international economic outlook and the characteristics of domestic regional and industrial economic development, we propose three policy recommendations to provide scientific reference for decision-making by relevant government departments.

Let $\mathbb{F}_{q}$ be the finite field of $q$ elements, and $\mathbb{F}_{q^{n}}$ be its extension of degree $n$. An element $\alpha\in \mathbb{F}_{q^{n}}$ is called a normal element of $\mathbb{F}_{q^{n}}/\mathbb{F}_{q}$ if $\{\alpha,\alpha^{q},\ldots, \alpha^{q^{n-1}}\}$ constitutes a basis of $\mathbb{F}_{q^{n}}/\mathbb{F}_{q}$. Normal elements over finite fields have proved very useful for fast arithmetic computations with potential applications to coding theory and to cryptography. The minimal polynomial of a normal element is certainly an irreducible polynomial with nonzero trace, while the converse does not hold in general. Using linearized polynomials, we give some necessary and sufficient conditions for this problem, which extend the known results.

Against the backdrop of economic globalization, the rapid advancement of cutting-edge digital technology has catalyzed a new wave of technological revolution. The AIGC technology, represented by ChatGPT, disrupts the technical landscape of traditional artificial intelligence. And it is widely embraced for its enhanced human-like functionalities, thus emerging as a pivotal milestone in the advancement of general artificial intelligence. Through the analysis of ChatGPT's impacts on the education and employment market, this research reveals that the implementation of AIGC technology can enhance social value exchange efficiency and invigorate the education and employment market. However, it also gives rise to legal and ethical concerns such as data privacy infringement. Therefore, management and supervision recommendations are proposed to address potential risks in order to ensure seamless operation of the economy and society.